Method for determining and tuning process characteristic parameters using a simulation system

ABSTRACT

A process characteristic parameter determination system uses a process model and a tuning module to accurately determine a value for a process characteristic parameter within a plant without measuring the process characteristic parameter directly, and may operate on-line or while the process is running to automatically determine a correct value of the process characteristic parameter at any time during on-going operation of the process. The process characteristic parameter value, which may be a heat transfer coefficient value for a heat exchanger, can then be used to enable the determination of a more accurate simulation result and/or to make other on-line process decisions, such as process control decisions, process operational mode decisions, process maintenance decisions such as implementing a soot blowing operation, etc.

TECHNICAL FIELD

The present disclosure relates generally to determining accurate processcharacteristic parameter values within power plants, industrialmanufacturing plants, material processing plants and other types ofplants and, more particularly, to determining process characteristicparameters using a tunable simulation system.

BACKGROUND

Generally speaking, processes used in power plants, industrialmanufacturing plants, material processing plants and other types ofplants include one or more controllers communicatively coupled to aplurality of field devices via analog, digital, combined analog/digital,or wireless communication channels or lines. The field devices, whichmay be, for example, valves, valve positioners, switches, transmitters(e.g., temperature, pressure, level and flow rate sensors), burners,heat exchangers, furnaces, boilers, turbines, etc., are located withinthe plant environment and perform process functions such as opening orclosing valves, measuring process parameters, generating electricity,burning fuel, heating water, etc., in response to control signalsdeveloped and sent by the controllers. Smart field devices, such as thefield devices conforming to any of the well-known Fieldbus protocols mayalso perform control calculations, alarming functions, and otherfunctions commonly implemented within or by a plant controller. Theplant controllers, which are also typically located within the plantenvironment, receive signals indicative of process measurements made bythe field devices and/or other information pertaining to the fielddevices and execute a control application that runs, for example,different control modules which make process control decisions, generateprocess control signals based on the received information and coordinatewith the control modules or blocks being performed in the field devices,such as HART® and FOUNDATION® Fieldbus field devices. The controlmodules within the controller send the process control signals over thecommunication lines or networks to the field devices to thereby controlthe operation of the process.

Information from the field devices and the controller is usually madeavailable over a data highway to one or more other computer devices,such as operator workstations, personal computers, data historians,report generators, centralized databases, etc., typically placed incontrol rooms or at other locations away from the harsher plantenvironment. These computer devices may also run applications that may,for example, enable an operator to perform functions with respect to theprocess, such as changing settings of the process control routine,modifying the operation of the control modules within the controllers orthe field devices, viewing the current state of the process, viewingalarms generated by field devices and controllers, implementingauxiliary processes, such as soot-blowing processes or other maintenanceprocesses, keeping and updating a configuration database, etc.

As an example, the Ovation® control system, sold by Emerson ProcessManagement, includes multiple applications stored within and executed bydifferent devices located at diverse places within a process plant. Aconfiguration application, which resides in one or moreengineer/operator workstations, enables users to create or changeprocess control modules and to download these process control modulesvia a data highway to dedicated distributed controllers. Typically,these control modules are made up of communicatively interconnectedfunction blocks, which are objects in an object oriented programmingprotocol, which perform functions within the control scheme based oninputs thereto and provide outputs to other function blocks within thecontrol scheme. The configuration application may also allow a designerto create or change operator interfaces which are used by a viewingapplication to display data to an operator and to enable the operator tochange settings, such as set points, within the process control routine.Each of the dedicated controllers and, in some cases, field devices,stores and executes a controller application that runs the controlmodules assigned and downloaded thereto to implement actual processcontrol functionality. The viewing applications, which may be run on oneor more operator workstations, receive data from the controllerapplication via the data highway and display this data to processcontrol system designers, operators, or users using the user interfaces.A data historian application is typically stored in and executed by adata historian device that collects and stores some or all of the dataprovided across the data highway while a configuration databaseapplication may execute in a still further computer attached to the datahighway to store the current process control routine configuration anddata associated therewith.

In many industries however, it is desirable or necessary to implement asimulation system for simulating the operation of a plant (including thevarious plant devices and the control network as connected within theplant) in order to perform better control of the plant, to understandhow proposed control or maintenance actions would actually affect theplant, etc. Such a simulation system may be used to test the operationof the plant in response to new or different control variables, such asset-points, to test new control routines, to perform optimization, toperform training activities, etc. As a result, many different types ofplant simulation systems have been proposed and used in process plants.

In the field of process plant simulation, process simulator design istypically based on either a first principle-based model or an empiricaldata-based model. A first principle-based model, also called ahigh-fidelity model, models equipment and processes based on firstprinciple physical laws, such as well known mass, energy, and momentumconservation laws. First principle-based models describing a physicalprocess are often complex and may be expressed using partialdifferential equations and/or differential algebraic equations. Theseequations may describe process or equipment properties and/or changes inthose properties. In many first principle-based models, equations aremodular which enables these equations to model specific pieces ofequipment and/or processes in a multi-equipment or multi-process system.Thus, equipment and/or processes can be easily changed and/or updated inthe model by replacing equations in the model with equationscorresponding to the changed and/or updated equipment and/or processes.However, first principle-based models are subject to modeling errors dueto the inability of first principle-based models to account foruncertainty surrounding the actual characteristics or properties ofprocess equipment or of the process environment at any particular time.These process characteristics are, in many cases, simply estimated by aplant operator or are estimated using some other manual or off-lineestimation technique.

On the other hand, empirical data-based models, also commonly calledblack-box models, generate modeling formulas or equations by applyingtest inputs to an actual process system in accordance with a designedexperiment and then measuring test outputs corresponding to the testinputs. Based on the inputs and outputs, equations or other models thatdefine a relationship between the inputs and outputs are generated tothereby create a model of the process or equipment. In this approach,the empirical equations may be easier to obtain than firstprinciple-based equations, and dynamic transient phenomena may be bettercaptured and represented in the empirical equations than in firstprinciple-based equations. However, special experiments must bedesigned, implemented, and executed to acquire the accurate and diversedata sufficient to generate the empirical data used to develop themodel. Moreover, the plant must typically be operated over some periodof time in order to develop the model, which can be expensive and timeconsuming. Further, when equipment is changed or replaced, new empiricalmodels must be developed, which can also be time consuming and costly.Still further, empirical models are unable to account for changes in theplant environment or for slow or gradual changes in the process plantequipment that result from aging or use of the plant equipment. In otherwords, while empirical based models are able to account for inherentprocess characteristics at the time of generation, these models areunable to be altered easily to account for changes in the processcharacteristics over time.

Thus, regardless of the type of process modeling approach used, aprocess simulation model often needs tuning and/or adjustment to beaccurate enough for the purposes of the simulation in which the model isused. For example, in many cases, the created simulation models includefactors related to unmeasureable process variables or characteristics(e.g., inherent properties of the process or the process equipment),referred to herein as process characteristic parameters, that changeover time due to, for example, wearing of equipment, changes in theplant environment, etc. An example of one such process characteristicparameter is a heat transfer coefficient used to model heat exchangerswithin a plant, although there are many other such processcharacteristic parameters. In many cases, while these processcharacteristic parameters are unmeasureable as such in the plant, it isimportant to be able to determine these process characteristicparameters accurately, as the values of these process characteristicparameters not only affect the accuracy of the simulation, but may alsobe used to make decisions to perform other actions within the plant,such as control actions and maintenance actions.

As an example, many power plant processes (as well as other types ofprocess applications) utilize heat exchangers that operate to transferthermal energy from one fluid medium to another fluid medium as part ofthe power generation process. It is important for simulation and controlpurposes to determine how much energy is being transferred between thefluids at any particular time so that the equipment efficiency and theresulting temperature change can be accurately simulated, evaluated orunderstood, to thereby be able to determine appropriate control and/ormaintenance actions. It is known that the heat transfer efficiency ofheat exchangers and the resulting medium temperature changes within aheat exchanger are largely affected by the material properties of theheat exchanger (such as heat conductivity and heat capacitance of theheat exchanger), the heat exchanger surface area, the thickness of theheat exchanger tubes, the heat exchanger geometry, and various run-timeconditions. Among these factors, material property, surface area, tubethickness, and configuration geometry can be considered to be designdata from which a “design” heat transfer coefficient for a heatexchanger can be determined based on known mathematical principles.However, design information usually provides only a coarse approximationto the actual heat transfer coefficient of a particular heat exchangerbeing used in a plant at any particular time. The reason that the designheat transfer coefficient and the actual heat transfer coefficient of aheat exchanger as used in a plant differ is that the design data doesnot account for other, typically changing, factors present within aprocess plant that alter or affect the heat transfer coefficient, andthus affect or alter the heat exchanger efficiency during operation ofthe plant. In fact, the actual run-time environment typically includesmany different factors that directly impact the “actual” heat transfercoefficient, which in turn affects the “actual” heat transfer efficiencyand the “actual” final temperature of the fluid exiting the heatexchanger. For example, as the result of harsh coal combustion andfly-ash within a flue gas entering into a heat exchanger, soot builds upon or deposits on the surfaces of heat exchanger, and the heat transfercharacteristics of this soot greatly affects the efficiency of andtemperature changes produced within the heat exchanger. In addition,soot build-up and soot-blowing operations (that are implemented fromtime to time to remove the soot build-up within a heat exchanger) changethe thickness of the tubes over time, which also affects the efficiencyand temperature profile of the heat exchanger. Thus, the “actual” heattransfer coefficient for a heat exchanger used in process simulation andcontrol needs to be adjusted or tuned to be different than the designheat transfer coefficient value for that heat exchanger to account forunmeasureable factors or phenomena present in the actual run-timesituation.

To deal with this problem, it is common in current industry practice totune and tweak the heat transfer coefficient in a heat exchanger model(which is usually a first principle-based model) using off-linecalculations that are performed on historical data collected for theplant. However, calculating or determining the heat transfer coefficientin this manner results in a delay in updating the model, meaning thatthe model is still typically out of tune when used in real-time.Moreover, this delay may result in incorrect tuning, as the heattransfer coefficient may have changed between the collection ofhistorical data and the running of the plant based on the heat transfercoefficient that was tuned or tweaked based on the historical data.

SUMMARY

A process characteristic parameter determination system accuratelydetermines an actual process characteristic parameter value that existswithin a plant without measuring the process characteristic parameterdirectly, and may operate on-line or while the process is running toautomatically provide a current value of the process characteristicduring on-going operation of the process. The process characteristicparameter value, as so determined, can then be used to enable thedetermination of a more accurate simulation result and/or to make otheron-line process decisions, such as process control decisions, processmaintenance decisions, process operational mode decisions, etc.

The process characteristic parameter determination unit includes aplant, equipment or process model, such as a first principle-basedmodel, that includes or uses at least one process characteristicparameter to determine a predicted process variable output, and includesa tuning module that tunes or changes the value of the processcharacteristic parameter within the process model based on actual plantoperation to thereby automatically adjust the value of the processcharacteristic parameter used in the process model to drive thepredicted value of the process variable to match the measured or actualplant operation. This process characteristic parameter determinationsystem thus automatically determines the values of one or more processcharacteristic parameters that make the output of the process modelmatch or follow measured plant operation, leading to a quick andautomatic determination of the process characteristic parameter during,for example, on-line operation of the plant. The process characteristicparameter values, as so determined or tuned, can then be used to performbetter or more accurate simulation, and can also be used to performother control or maintenance procedures. For example, when a heattransfer coefficient for a heat exchanger is determined using theprocess characteristic parameter determination system described herein,this heat transfer coefficient can be used to determine when to performsoot blowing or other maintenance procedures on the heat exchanger tothereby increase the efficiency of the plant operation, to reduce wearand tear on the heat exchanger caused by unnecessary soot blowingoperations, etc. This heat transfer coefficient can also or instead beused to perform better or different types of control in a situation inwhich, for example, different types of control may be more suitable forcontrolling the process depending on the value or state of the processcharacteristic parameter. Still further, the determined processcharacteristic parameter may be used to decide whether or not to operatethe plant or some portion of the plant at all, or to change theoperational mode of the plant or some portion of the plant to therebyoperate the plant more profitably, for example.

Still further, a simulation system and method defines or uses a processmodel, such as a first principle-based model, for modeling the operationof the plant or a relevant portion of the plant, such as a unit, a pieceof equipment, a control system, etc. of the plant. The process modelincludes one or more variables indicative of a process characteristicparameter that is used in the modeling or that is used to determine oraffect the output of the model. The process model is then operated alongwith the plant, and the simulation system and method compares an outputof the process model with a measured or determined plant variable (whichmay be a measured process variable or a control signal) to determine adifference or error between the simulated or modeled process variableand the measured process variable value. The simulation system andmethod then use this difference in a tuning module to alter the value ofthe process characteristic parameter as used within the process model ina manner that drives the output of the process model to match themeasured process variable value. In essence, the tuning module performsfeedback compensation on the process characteristic parameter value todrive the model output of the process variable towards the measuredprocess variable value. When the output of the process model matches orclosely follows the measured process variable value, the simulationsystem and method may indicate that the value of the processcharacteristic parameter is correct, and may provide this processcharacteristic parameter value to a controller, a user interface system,or another simulation system for use in controlling the process, fordisplay to the user, for simulating the process more accurately, etc. Inaddition, the process characteristic parameter value may be used (eithermanually or automatically) to make other decisions, such as controldecisions, maintenance decisions (e.g., whether to implement a sootblowing operation on a heat exchanger), or business decisions (e.g.,whether to continue to run or to stop running the plant in the currentmode because it is no longer profitable to do so).

In effect, an automatic, on-line tuning method for a simulation systemmonitors the difference between a model predicted process variablevalue, such as a temperature profile of an output of a heat exchanger,and a measured process variable value of the process variable, e.g., ameasured temperature of the output of the heat exchanger of the plant,in real-time. The tuning method utilizes a feedback controller whichoperates on this difference to gradually adjust the processcharacteristic parameter value, such as a heat transfer coefficient, ofthe process model used in the simulation system, until the modeledprocess variable value matches the actual or measured process variablevalue (e.g., the temperature as measured in the process). When these twovariable values match, the process characteristic parameter value of themodel (e.g., the modeled heat transfer coefficient) can be treated asaccurate at that moment or for a short time period. Moreover, when thesimulation and tuning procedure is carried out in real-time, the newlycalculated process characteristic parameter value (e.g., the heattransfer coefficient) can be regarded as adaptively tracking the actualprocess characteristic parameter (e.g., the actual heat transfercoefficient of the plant). As a result, this scheme can assist inproviding better control and maintenance procedures in plants, as thisscheme accurately models or determines plant (e.g., equipment)characteristics that affect the need for control or maintenanceoperations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example process plant and controlnetwork, such as that for a power plant, in which a simulation systemcan be implemented to compute or predict one or more processcharacteristic parameter values.

FIG. 2 is a block diagram of various components of a boiler steam cycleof a typical boiler based power plant that may be simulated using thesimulation system implemented in the plant of FIG. 1.

FIG. 3 is a block diagram of a set of simulation modules of adistributed simulation system configured to simulate the operation ofthe boiler steam cycle of the power plant of FIG. 2.

FIG. 4 is a block diagram of a first example simulation module of thesimulation system of FIG. 3 as connected within a process, wherein thesimulation module includes a process model and a tuning module thatdetermines a correct value for a process characteristic parameter usedwithin the process model based on plant operational conditions.

FIG. 5 is a block diagram of a second example simulation module of thesimulation system of FIG. 3 as connected within a process, wherein thesimulation module includes a process model and a tuning module thatdetermines a correct value for a process characteristic parameter usedwithin the process model based on plant operational conditions.

FIG. 6 is a graph illustrating an example operation of the simulationmodule of FIG. 4.

DETAILED DESCRIPTION

Referring now to FIG. 1, an example process plant and control networkfor a plant 10, such as that associated with a power generation plant,an industrial manufacturing plant, a chemical processing plant, etc., isillustrated at an abstract level of detail to illustrate a plant inwhich the process characteristic parameter determination unit andsimulation system described herein can be implemented. The plant 10includes a distributed control system having one or more controllers 12,each of which is connected to one or more field devices 14 and 16 viainput/output (I/O) devices or cards 18 which may be, for example,Fieldbus interfaces, Profibus® interfaces, HART® interfaces, standard4-20 ma interfaces, etc. The controllers 12 are also coupled to one ormore host or engineer/operator workstations 20, 21 and 22 via a datahighway 24 which may be, for example, an Ethernet link. A database 28may be connected to the data highway 24 and operates as a data historianto collect and store parameter, process variable (including processvariable measurements and control signals), status and other dataassociated with the controllers 12 and field devices 14, 16 within theplant 10. Additionally or alternatively, the database 28 may operate asa configuration database that stores the current configuration of theprocess control system within the plant 10 as downloaded to and storedwithin the controllers 12 and field devices 14 and 16. While thecontrollers 12, the I/O cards 18 and the field devices 14 and 16 aretypically located down within and are distributed throughout thesometimes harsh plant environment, the engineer/operator workstations20, 21 and 22 and the database 28 are usually located in control roomsor other less harsh environments easily assessable by controller ormaintenance personnel.

As is known, each of the controllers 12, which may be by way of example,the Ovation® controller sold by Emerson Process Management Power andWater Solutions, Inc., stores and executes a controller application thatimplements a control strategy using any number of different,independently executed, control modules or blocks 29. Each of thecontrol modules 29 can be made up of what are commonly referred to asfunction blocks wherein each function block is a part or a subroutine ofan overall control routine and operates in conjunction with otherfunction blocks (via communications called links) to implement processcontrol loops within the process plant 10. As is well known, functionblocks, which may but need not be objects in an object orientedprogramming protocol, typically perform one of an input function, suchas that associated with a transmitter, a sensor or other processparameter measurement device, a control function, such as thatassociated with a control routine that performsproportional-integral-derivative (PID), fuzzy logic, etc. control, or anoutput function that controls the operation of some device, such as avalve, to perform some physical function within the process plant 10. Ofcourse hybrid and other types of complex function blocks exist such asmodel predictive controllers (MPCs), optimizers, etc.

In the plant 10 illustrated in FIG. 1, the field devices 14 and 16connected to the controllers 12 may be standard 4-20 ma devices, may besmart field devices, such as HART®, Profibus®, or FOUNDATION® Fieldbusfield devices, which include a processor and a memory, or may be anyother desired type of field devices. Some of these devices, such asFieldbus field devices (labeled with reference number 16 in FIG. 1), maystore and execute modules, or sub-modules, such as function blocks,associated with the control strategy implemented in the controllers 12.Function blocks 30, which are illustrated in FIG. 1 as being disposed intwo different ones of the Fieldbus field devices 16, may be executed inconjunction with the execution of the control modules 29 within thecontrollers 12 to implement one or more process control loops, as iswell known. Of course, the field devices 14 and 16 may be any types ofdevices, such as sensors, valves, transmitters, positioners, etc., andthe I/O devices 18 may be any types of I/O devices conforming to anydesired communication or controller protocol such as HART®, Fieldbus,Profibus®, etc.

Still further, in a known manner, one or more of the workstations 20-22may include user interface applications to enable a user, such as anoperator, a configuration engineer, a maintenance person, etc., tointerface with the process control network within the plant 10. Inparticular, the workstation 22 is illustrated as including a memory 34which stores one or more user interface applications 35 which may beexecuted on a processor 46 within the workstation 22 to communicate withthe database 28, the control modules 29 or other routines within thecontrollers 12 or I/O devices 18, with the field devices 14 and 16 andthe modules 30 within these field devices, etc., to obtain informationfrom the plant, such as information related to the ongoing state of theplant equipment or the control system. The user interface applications35 may process and/or display this collected information on a displaydevice 37 associated with one or more of the workstations 20-22. Thecollected, processed and/or displayed information may be, for example,process state information, alarms and alerts generated within the plant,maintenance data, etc. Likewise, one or more applications 39 may bestored in and executed in the workstations 20-22 to performconfiguration activities such as creating or configuring the modules 29and 30 to be executed within the plant, to perform control operatoractivities, such as changing set-points or other control variables,within the plant, etc., to perform maintenance applications orfunctions, such as running soot-blowers in the plant, initiatingcontroller tuning within the plant, running valve or other equipmentmaintenance procedures, etc. Of course the number and type of routines35 and 39 is not limited by the description provided herein and othernumbers and types of process control and maintenance related routinesmay be stored in and implemented within the workstations 20-22 ifdesired.

The workstations 20-21, the database 28 and some of the controllers 12of FIG. 1 are also illustrated as including components of a processcharacteristic parameter determination system and/or a simulation systemthat may be implemented in plant, such as that of FIG. 1. If desired,the process characteristic parameter determination system may beperformed as part of or within a simulation system, which may be acentralized simulation system, in which case the simulation systemcomponents may be stored and executed in one of the operatorworkstations 20-22, for example, in a controller 12, in the datahistorian 28 or in any other computer or processing device within theplant 10 or in communication with the plant 10. On the other hand, thesimulation system may be a distributed simulation system in which casesimulation system components may be stored in various different ones ofthe devices associated with the plant 10, such as in the field devices14, 16, the controllers 12, the user interfaces 20-22, the datahistorian 28, etc.

As one example, the workstation 20 is illustrated as including a set ofsimulation support applications 40, which may include a simulationconfiguration application, a user interface application and datastructures for performing simulation of the process plant 10 using aprocess characteristic parameter determination unit in a mannerdescribed herein. Generally speaking, the simulation applications 40enable a user to create, implement and view the results of a simulationexecuted by the various simulation components within the computernetwork system of FIG. 1. More particularly, a distributed simulationsystem may include various distributed simulation modules 42 that may belocated in various different computing devices (also referred to asdrops) on the computer network of FIG. 1. Each of the simulation modules42 stores a model that is implemented to simulate the operation of anindividual plant component or group of components, and the simulationmodules 42 communicate directly with one another to implement asimulation of a larger portion of the plant 10. Any particularsimulation module 42 may be used to simulate any portion or part of theplant 10, including a particular piece of plant equipment involved inprocessing or material flow, such as a tank, a heat exchanger, acontroller, etc., or a group of equipment, such as a unit. Stillfurther, the simulation modules 42 may be located in and executed invarious different devices or drops on the computer network and maycommunicate via, for example, the communication bus 24, to send databetween the simulation modules 42 so as to perform simulation of alarger group or set of plant equipment. Of course, any desired number ofsimulation modules 42 may be located in any particular drop and eachdrop may execute the simulation modules 42 therein independently of theother drops, so as to implement distributed simulation. However, ifdesired, all of the simulation modules 42 associated with any particularsimulation may be stored in an executed by the same computer device(i.e., at a single drop) and still be implemented in the mannerdescribed herein.

The simulation applications 40 may be accessed by any authorized user(such as a configuration engineer, an operator or some other type ofuser) and may be used to create and configure a particular instance of asimulation system, by creating a set of simulation modules 42 anddownloading these modules 42 to different drops within the plant orcomputer network. As illustrated in FIG. 1, various ones of thedistributed simulation modules 42 may be downloaded to and implementedin the workstations 20-22, the controllers 12, the database 28 and/orany other computer device or processing device connected to thecommunication network 24. If desired, simulation modules 42 may belocated and implemented in other processing devices that are indirectlyconnected to the network 24, such as in the field devices 16, in abusiness local area network (LAN) or even a wide area network (WAN)connected to one of the devices on the network 24. Still further, whilethe bus or network 24 is illustrated in FIG. 1 as the main communicationnetwork used to connect various drops that implement simulation modules,other types of communication networks could be used to connect drops,including any desired LANs, WANs, the internet, wireless networks, etc.

Once downloaded, the distributed simulation modules 42 executeindividually but operate in conjunction with one another to performsimulation of the plant or components and equipment within the plant, asbeing controlled by the control blocks 29 and 30 as well as othercontroller routines executed within the controllers 12 and possibly thefield devices 14, 16. Such a distributed simulation system may enable auser to perform different simulation and prediction activities withrespect to the plant 10, via a user interface application in the suiteof simulation applications 40. If desired, a distributed simulationsystem may simulate an operating plant or any portion thereof, such asthat illustrated in FIG. 1, or may simulate a plant that has notactually been constructed. Importantly, as described in further detailherein, one or more of the simulation modules 42 may include orimplement a process characteristic parameter determination unit ortechnique to accurately determine a current value of a processcharacteristic parameter that is used in the process model of thatsimulation module. The simulation modules 42 that include a processcharacteristic parameter determination unit or that implement a processcharacteristic parameter determination technique may provide thedetermined or current value of the process characteristic parameter toother simulation modules 42, to one or more of the user workstations20-22, to one or more of the user interface applications 35, to one ormore of the maintenance, control or configuration applications 39, oreven to one or more of the controller applications 29 used to controlthe plant. These various different applications may receive and use thedetermined process characteristic parameters in any desired or usefulmanner, such as by displaying the current value of the processcharacteristic parameters to a user via a user interface application 35to enable the user to take some maintenance, simulation or controlaction, by automatically taking control actions (e.g., tuning acontroller, changing a mode of a controller, etc.) or maintenanceactions (e.g., implementing one or more maintenance procedures, such asa soot blowing operation on a heat exchanger), or business actions(e.g., determining whether to continue to run or to stop running theplant in the current mode because it is no longer profitable to do so),etc.

As a more particular example of a plant in which a processcharacteristic determination unit can be used as part of a simulationsystem, FIG. 2 illustrates a block diagram of a boiler steam cycle for atypical boiler 100 that may be used, for example, by a thermal powergeneration plant which may be implemented using the process plant andcontrol network of FIG. 1, for example. The boiler 100 includes varioussections through which steam or water flows in various forms such assuperheated steam, reheat steam, etc. While the boiler 100 illustratedin FIG. 2 has various boiler sections situated horizontally, in anactual implementation, one or more of these sections may be positionedvertically, especially because flue gases heating the steam in variousboiler sections, such as a water wall absorption section, risevertically.

In any event, the boiler 100 illustrated in FIG. 2 includes a water wallabsorption section 102, a primary superheat absorption section 104, asuperheat absorption section 106 and a reheat section 108 all of whichinclude various heat exchangers 109 a-109 d. Additionally, the boiler100 includes one or more de-superheaters 110 and 112 and an economizersection 114. The main steam generated by the boiler 100 is used to drivea high pressure (HP) turbine 116 and the hot reheat steam coming fromthe reheat section 108 is used to drive an intermediate pressure (IP)turbine 118. Typically, the boiler 100 may also be used to drive a lowpressure (LP) turbine, which is not shown in FIG. 2.

The water wall absorption section 102, which is primarily responsiblefor generating steam, includes a number of pipes through which steamenters a drum. The feed water coming into the water wall absorptionsection 102 may be pumped through the economizer section 114. The feedwater absorbs a large amount of heat when in the water wall absorptionsection 102. For example, in a typical drum-type boiler, the water wallabsorption section 102 has a steam drum, which contains both water andsteam, and the water level in the drum has to be carefully controlled.The steam collected at the top of the steam drum is fed to the primarysuperheat absorption section 104, and then to the superheat absorptionsection 106, which together raise the steam temperature to very highlevels using various heat exchangers 109 a and 109 b. The water at thebottom of the drum is recirculated and heated further in one of the heatexchangers 109 c. The main steam output from the superheat absorptionsection 106 drives the high pressure turbine 116 to generateelectricity.

Once the main steam drives the HP turbine 116, the exhaust steam isrouted to the reheat absorption section 108 where this steam is heatedfurther in heat exchangers 109 d, and the hot reheat steam output fromthe reheat absorption section 108 is used to drive the IP turbine 118.The de-superheaters 110 and 112 may be used to control the final steamtemperature to be at desired set-points. Finally, the steam from the IPturbine 118 may be fed through an LP turbine (not shown here) to a steamcondenser (not shown here), where the steam is condensed to a liquidform, and the cycle begins again with various boiler feed pumps pumpingthe feed water for the next cycle. The economizer section 114 is locatedin the flow of hot exhaust gases exiting from the boiler and uses thehot gases to transfer additional heat to the feed water before the feedwater enters the water wall absorption section 102.

FIG. 3 illustrates a set of simulation modules 42 that may be used orimplemented in a distributed manner to simulate the operation of theboiler steam cycle of FIG. 2. As will be understood, various of thesimulation modules 42 may include or implement a process parametercharacteristic determination unit or technique that determines a valuefor a process parameter characteristic within the plant. In particular,various of the simulation modules 42 of FIG. 3 may determine current orcorrect values for heat transfer coefficients for the various heatexchangers 109 a-109 d of FIG. 2.

As can be seen in FIG. 3, the distributed simulation modules 42 includeseparate simulation modules for each of the main plant elements depictedin FIG. 2 including a water wall absorption simulation module 102S, aprimary superheat absorption simulation module 104S, a superheatabsorption simulation module 106S, a reheat absorption simulation module108S, desuperheater simulation modules 1105 and 1125, an economizersimulation module 1145, and turbine simulation modules 1165 and 1185. Ofcourse, these simulation modules include plant element models, which maybe in the form of first-principle equations, or any other desired typesof models such as empirical models, which model the operation of theseelements to produce simulated outputs for the corresponding plantequipment of FIG. 2 based on the inputs provided thereto. As will beunderstood, these process models include or use, in some manner, aprocess characteristic parameter, such as a heat transfer coefficient ofone or more of the heat exchangers 109 a-109 d, to perform modeling ofthe relevant portion of the plant. While a separate simulation module isillustrated in FIG. 3 for each of the major plant components of FIG. 2,simulation modules could be made for sub-components of these thecomponents of FIG. 2 or a single simulation module could be createdcombining multiple ones of the plant components of FIG. 2.

Thus, generally speaking, the distributed simulation technique andsystem illustrated in the example of FIG. 3 uses a number of separatesimulation modules, wherein each simulation module models or representsa different active component in the process or plant being simulated(referred to herein as plant element simulation modules) or models.During operation, each simulation module may be executed separately,either in a common machine or processor or in separate machines orprocessors, to thereby enable parallel and distributed processing. Thus,for example, the different simulation modules of FIG. 3 can be executedin different and various ones of the workstations 20-22 of FIG. 1, thecontrollers 12 of FIG. 1, the field devices 16 of FIG. 1, the database28 of FIG. 1, etc.

As indicated above, and as illustrated in FIG. 3, each of the simulationmodules of FIG. 3 includes one or more executable models 202 formodeling the operation of an associated plant element or pipe and thesimulation modules operate to implement these model(s) 202 to simulateoperation of a plant element based on the inputs delivered to the plantelement (in the form of fluids, solids, control signals, etc.) In mostcases, the simulation modules will perform simulation based on anindication of an input (e.g., a fluid input, a gas input, a fluidpressure, temperature, etc.) from an upstream simulation module and willproduce one or more output indications, indicating an output of theprocess or plant element (e.g., in the form of a fluid output, e.g., gasoutput, such as a fluid temperature, pressure, etc.) The models 202 usedin the plant element simulation modules may be first principle models orany other suitable type of models for the particular piece of equipmentbeing simulated. However, to implement the process characteristicparameter determination technique described herein, various of thesimulation modules 102S to 118S may be tied to or receive processvariable measurements (in the form of sensor measurements, processcontroller inputs and outputs, etc.) from the plant being simulated.

The distributed simulation system of FIG. 3 is also illustrated asincluding pipe simulation modules P1-P8 which are disposed between theplant element simulations modules described above. Generally speaking,the pipe simulation modules P1-P8, which can serve as simulationboundary modules, are responsible for modeling flow between the othersimulation modules, providing feedback from downstream simulationmodules to upstream simulation modules and implementing mass flow andmomentum balancing equations to equalize the simulations performed bythe different plant element simulation modules 102S-118S. The pipesimulation modules P1-P8 include models 202 for performing this flowmodeling, e.g., that implement mass flow and momentum balancing routinesto balance the mass flow, pressures, etc. between the differentsimulation modules 102S-118S. As also illustrated in FIG. 3, the varioussimulation modules 102S-118S and P1-P8 are connected via inputs andoutputs 210. The specific operation of the distributed simulation systemof FIG. 3 is provided in more detail in U.S. Patent ApplicationPublication No. 2011/0131017 and in particular in the description ofFIGS. 3-8 of that publication, the contents of which is hereby expresslyincorporated by reference herein. However, in this case, one or more ofthe simulation modules 102S-118S or even the simulation modules P1-P8include a process model that uses a process characteristic parameter toperform modeling or prediction and these simulation modules include atuner that tunes this process characteristic parameter in a manner thatforces the process characteristic parameter of the process model tomatch, equal or closely approximate the actual value of that processcharacteristic parameter within the actual process.

Referring now to FIG. 4, a process characteristic parameterdetermination unit is illustrated in the form of a simulation module 400which is coupled to a process 404 and between an upstream simulationboundary module 406 and a downstream simulation boundary module 408. Thesimulation module 400 may be, for example, any of the simulation modules102S-118S if so desired, while the boundary modules 406 and 408 may beany appropriate ones of the simulation modules P1-P8 of FIG. 3. However,these elements could be other types of simulations modules and boundarymodules as well or instead. For clarity purposes, simulated or predictedsignals or values generated by the simulation system are illustratedwith dotted lines in FIGS. 4 and 5, while process control signals (e.g.,signals or values determined within or measured by the process controlsystem) are indicated with solid lines in FIGS. 4 and 5.

Generally speaking, the simulation module 400, which in this case is apart of a distributed simulation system, includes a process model 410, adifference unit or summer 412 and a simulation model tuner unit 414which operate together to determine a value for a process characteristicparameter used by the process model 410. In this example, the process404 may be an on-line process that is operating in real time and towhich the simulation module 400 is connected via a communication channelor line. In this case, the simulation module 400 receives data (e.g.,control signal data and measured process variable data) from the process404 in real-time or as the process 404 is operating on-line and thus itmay be beneficial to store and execute the simulation module 400 in thedevice that implements the process controller for the process 404 toreduce inter-device data communications within the simulation andcontrol system of the plant. Alternatively, the process 404 may be anoff-line process connected to the simulation module 400 via, forexample, a data historian, such as the data historian 28 of FIG. 1. Inthis case, the simulation module 400 receives data collected from theprocess 404 in the past and stored in the data historian 28, and here itmay be beneficial to store and execute the simulation module 400 in thedata historian device to reduce data communications within thesimulation and control system.

As indicated in FIG. 4, the process model 410 receives model inputs, viaan input line or communication channel or connection 416, from theupstream simulation boundary module 406, which may be, for example, oneof the other simulation modules of the distributed simulation system ofFIG. 3, to which the simulation module 400 is communicatively connected.Still further, the process model 410 receives, via an input line orcommunication channel or connection 418, control signals output from aprocess controller 420 that operates as part of the process 404 tocontrol the process 404 or elements within the process 404, such asvalves, burners, fluid flows, etc. Generally speaking, the controlleroutputs (process control signals) provided at the input line 418 arereceived via or from the process controller 420 that controls or effectsthe portion of the process 404 (e.g., equipment within the process 404)being modeled by the process model 410. As will be understood, theprocess model 410 may be any type of process model, including a firstprinciples-based model, or a black box or empirical-based model. Forexample, the process model may be based on or implement first principlemathematical equations as part of the modeling process (a firstprinciple-based model) or may, for example, be a transfer function basedmodel, a neural network model, a model predictive control (MPC) model, aregression model, a partial least squares (PLS) model, etc. (allexamples of empirical-based models). In the example of FIG. 4, theprocess model 410 is the main or sole part of a process model unitwithin the simulation module 400. In any case, the process model 410includes, in some form, one or more process characteristic parametersthat are used as part of the process model 410 to perform modeling ofthe process 404. For example, a process characteristic parameter used inthe process model 410 may be a heat transfer coefficient of a heatexchanger within the process 404 when, for example, the process model410 models one or more of the heat exchangers 109 a-109 d in, forexample, the boiler system of FIG. 2. In the case of an empirical-basedmodel, such as a transfer function based model, the processcharacteristic parameter may be, for example, a gain of the model (whichgain is considered to reflect a process characteristic).

As also illustrated in FIG. 4, the difference unit 412 (which may be asimple summing circuit or algorithm) includes two inputs and receives anoutput from the model 410 in the form of, for example, a predictedprocess variable value as developed by the process model 410 at oneinput and receives a measured process variable value (as measured withinthe process 404) for the same process variable being estimated by theprocess model 410 at another input. As will be understood, the processmodel 410 produces the prediction of the process variable based on theinformation from the control system 420, such as the process controlsignal being delivered to the process 404, based on information from thesimulation boundary module 406, such as the flow rates and otherupstream process variables needed by the process model 410 to operate tomake a prediction of the process variable based on current processconditions, based on the internally stored process model and, in somecases, based on information from the simulation boundary module 408. Thedifference determination unit 412 determines a difference or errorbetween the two inputs provided thereto, i.e., the measured processvariable value from the process 404 and the predicted process variablevalue as produced by the process model 402, and provides the errorsignal to the simulation model tuner 414. As will be understood, theprocess variable that is measured within the process 404 and the processvariable predicted by the process model 410 are related to the sameprocess variable and, in the example in which the model 410 is modelingthe operation of a heat exchanger, may be the temperature of the fluidat the output of the heat exchanger. Of course, the process variablevalue predicted by the model 410 and the process variable value asmeasured within the process 404 may be any other process variableincluding a control signal. In this example, it is assumed that theprocess variable value being predicted by the process model 410 and theprocess variable value measured in the process 404 is an uncontrolledprocess variable, that is, a process variable that is not being directlycontrolled by the control system 420 of the process 404. However, it maybe possible to provide measured and predicted values of controlledprocess variables to the unit 412 as well.

The simulation model tuner 414 includes tuner logic 425 that operates totune the value of the process characteristic parameter as used withinthe process model 410 in a manner that drives or causes the differenceor error signal produced by the difference unit 412 to zero. Inparticular, the tuner logic 425 of the simulation model tuner 414 mayalter the value of the process characteristic parameter (e.g., a heattransfer coefficient of a heat exchanger) as used within the processmodel 410 in order to cause the predicted process variable value outputby the process model 410 to more accurately or closely match the actualmeasured process variable value from the process 404. Of course, thetuner logic 425 of the simulation model tuner 414 may alter or adjustthe process characteristic parameter value within or used by the processmodel 410 in any known or desired manner, such as gradually, in fixed orvariable steps or otherwise, and may do so based on a predetermined setof rules or a control routine stored within the simulation module tuner414. Thus, for example, the tuner logic 425 of the simulation modeltuner 414 may store rules indicating, for example, how best to modifythe value of the process characteristic parameter in light of the errorsignal, and these rules may, for example, indicate that a positive errorsignal should cause the process characteristic parameter value to bealtered in one direction, while a negative error signal should cause theprocess characteristic parameter value to be altered in the oppositedirection. Additionally, the tuner logic 425 of the simulation modeltuner 414 may include and use any kind of feedback control routine, suchas a proportional, integral and/or derivative (PID) control routine, todetermine the best manner for altering the process characteristicparameter in a manner that best drives the process model 410 over timeto produce a predicted process variable value that matches or that isequal to the measured process variable value of process 404.

In any event, over time, by adaptively tuning or varying the processcharacteristic parameter value within the process model 410 to drive thepredicted process variable output of the process model 410 to match theprocess variable value as measured within the process 404, thesimulation model tuner 414 determines the actual value of the processcharacteristic parameter within the process 404. Thus when thesimulation model tuner 414 actually gets the process characteristicparameter within the process model 410 to a value that causes thepredicted process variable value at the output of the process model 410to match the measured process variable from the process 404 for thatsame process variable, resulting in a zero or near-zero error signaloutput by the difference unit 412, the simulation model tuner 414 has ineffect determined the actual value of the process characteristicparameter that is present in, exists in or is associated with thecurrent state of the process 404.

As illustrated in FIG. 4, the simulation module 400 may provide thecurrent value of the process characteristic parameter as well as otherneeded or pertinent model parameters or outputs, such as fluid flows,temperatures, pressures, etc. to the downstream simulation boundarymodule 408 for use in the next or downstream simulation element via acommunication channel, line or other connection. However, the simulationmodule 400 may also or instead provide the current value of the processcharacteristic parameter to any other simulation module or to any otherapplication or user within the process plant 10 of, for example, FIG. 1,for use in these other applications in any desired manner via one ormore communication channels, connections or lines associated with theprocess control network.

Of course, while the process characteristic parameter valuedetermination unit described in FIG. 4 is illustrated as being part of adistributed simulation module 400 which is an element within adistributed simulation system, this process parameter characteristicdetermination unit may be used as a stand-alone unit to determine anyparticular process characteristic. Moreover, the process characteristicparameter determination unit described herein with respect to FIG. 4could also be part of a centralized simulation system in whichsimulation is performed for a large portion of the plant or process, orfor the entire plant or process at a single location. Thus, the processcharacteristic determination unit used in the simulation module 400 maybe implemented as part of a centralized simulation system instead ofbeing part of a distributed simulation system as illustrated in FIG. 4.

The process 404 is indicated in FIG. 4 to be an online and operatingprocess, and thus in this case the process characteristic parametervalue determination unit determines the process characteristic parametervalue on-line, that is, during on-line or on-going or real-timeoperation process 404. However, the process characteristic parameterdetermination unit may also be communicatively coupled to the process404 via a data historian, such as the data historian 28 of FIG. 1 toreceive previously collected data for the process 404 to thereby operateoff-line to determine the process characteristic parameter for theprocess 404 at a time associated with the actual measured values of theprocess 404 stored in the historian 28. That is, the processdetermination characteristic unit may be operated in real-time andduring on-going operation of the process 404 for real-time determinationof the process characteristic parameter value, but may also beimplemented off-line or be implemented based on historical or previouslycollected and stored process variable data in the form of measuredprocess variables and control system inputs and outputs, as well asother data necessary for operation of the process model 410.

FIG. 5 illustrates another embodiment of a process characteristicparameter determination unit, again illustrated as part of a simulationmodule 500, which in this case may be used to determine processcharacteristic parameter values based on measured or controlled processvariables. As illustrated in FIG. 5, the simulation module 500 isconnected to a process 504 and between an upstream simulation boundarymodule 506 and a downstream simulation boundary module 508. Thesimulation module 500 includes a process model unit that includes aprocess model 510, which may be similar to the process model 410 of FIG.4 and the simulation module 500 includes a difference unit 512 and asimulation model tuner 514 which operate similar to the units 412 and414 of FIG. 4, respectively. Again the upstream simulation boundarymodel 506 provides the process model 510, via an input line orcommunication connection 516 with the necessary information for theprocess model 510 to operate including, for example, simulated flows,materials, temperatures, pressures, settings, etc., as determined by orused by upstream simulation modules. Similarly, such information issometimes also provided by the downstream simulation boundary module508.

However, as illustrated in FIG. 5, instead of receiving a control systemoutput from the process 504 and predicting, from this control systemoutput, a predicted value of an uncontrolled process variable (asperformed by the simulation module 400 of FIG. 4), the process model 510produces an estimate or estimated value of a controlled process variableand provides this predicted process variable value to a simulatedcontroller 520A (which may be part of the process model 510 or part ofthe process model unit of the simulation module 500). More particularly,the process model unit of the simulation module 500 includes a simulatedcontroller 520A which receives a controller set point for the controlledprocess variable from the process control system of the process 504 andreceives a predicted value of the controlled process variable asdeveloped by the process model 510. The simulated controller 520Aoperates to simulate the control actions or activities performed by anactual process controller 520B that is used to control the process 504using the same set point value for the controlled process variable. Ofcourse, the simulated controller 520A includes controller logic 524Athat operates in the same manner as actual controller logic 524B of theprocess controller 520B, that is, using the same logic, tuning factors,gains, settings, etc., because the purpose of the simulation controller520A is to simulate the operation of the actual controller 520B duringoperation of the process 504 based on the predicted value of controlledprocess variable (as output by the process model 510) instead of theactual or measured value of the controlled process variable.

As illustrated in FIG. 5, the predicted or simulated control signalvalues developed by the simulated process controller 520A are alsoprovided back to the process model 510 which uses these control signalvalues to produce the estimated value of the controlled processvariable. Moreover, the actual process controller 520B also receives thecontrol set point and a measurement of the controlled process variablefrom the process 504 as a feedback input and uses these signals toperform control of the process in any desired or known manner. Thus, aswill be understood, the actual controller 520B and the simulatedcontroller 520A use the same control parameters and control techniquesto perform control and to thus produce a control signal (that is, anactual control signal or a simulated control signal) based on the actualmeasurement of the controlled process variable from the process 504 orbased on the predicted value of the controlled process variable from theprocess model 510. Of course, the actual process controller 520Bproduces an actual control signal provided to the plant or process 504,while the simulated process controller 520A produces a simulated controlsignal, which is provided back to the process model 510 to enableprocess model 510 to produce the predicted value of the controlledprocess variable as an output.

As illustrated in FIG. 5, the control signals output from both theactual process controller 520B and the simulated process controller 520Aare provided as inputs to two inputs of the difference unit 512, whichproduces an error signal indicating the difference between the outputs(process control signal values) of the controllers 520A and 520B. Thisdifference or error signal is then provided to the simulation modeltuner 514, which, similar to simulation model tuner 414 of FIG. 4, usestuner logic 525 to alter or vary the value of the process characteristicparameter within the model 510 in an attempt to drive the output of thesimulated controller 520A to meet or match the output of the actualprocess controller 520B. The tuner logic 525 of the simulation modeltuner 514 may operate in a similar manner as that described with respectto the tuner 414 of FIG. 4 and thus can operate in any desired manner toalter or change the value of the process characteristic parameter withinthe process model 510. Of course, when the simulation model tuner 514causes the output of the simulated process controller 520A to be equalto or close to the output of the actual process controller 520B, thedifference unit 512 produces a zero or near-zero value, which indicatesthat the process characteristic parameter value of the model 510 is atthe real or actual value as it exists within the process 504. In thiscase, of course, the predicted value of the controlled process variableoutput by the model 510 should be equal or nearly equal to the value ofthe actual measured and controlled process variable within the process504 as, ideally, the controllers 520A and 520B operate the same on thesevalues to produce the same control signals (actual and simulated) beingdelivered to the difference detection unit 512.

Similar to the system of FIG. 4, the process model 510 (or thesimulation module 500 as a whole) may provide its outputs, includingflows, temperatures, etc., as well as, if desired, the current value ofthe process characteristic parameter to the downstream simulationboundary module 508, which is downstream of the simulation element ormodule 500 implementing the process parameter characteristicdetermination unit, via any desired communication channel or connection.Thus, as will be understood, the system of FIG. 4 enables a processcharacteristic parameter value to be determined using measurements of anuncontrolled process variable and a prediction value of an uncontrolledprocess variable as produced by a process model 410, while the system ofFIG. 5 enables a process characteristic parameter value to be determinedusing measurements of a controlled process variable and a predictionvalue of a controlled process variable as produced by a process model510. In the second case, the control signals output by the processcontroller 520B (as measured or determined within the process 504) andas produced by the simulation process controller 520A are compared toone another to determine whether the process characteristic parameterwithin the process model is set to the correct or actual valueassociated with that characteristic of the actual process. Of course, inall or most other manners, the elements of FIG. 5 are the same orsimilar to those of the corresponding elements of FIG. 4.

FIG. 6 depicts a graph 600 illustrating a simulated example of theoperation of the process characteristic parameter determination systemof FIG. 4 when used to determine a heat transfer coefficient of a heatexchanger in which flue gas produced in a boiler system of a power plantwas provided as an input to the heat exchanger to create or to heatsteam delivered to the output of the heat exchanger, wherein the steamwas used to drive one or more turbines of the power plant. Moreparticularly, a super-heater section of a boiler was selected for thisproto-type demonstration. The graph 600 of FIG. 6 includes an upper line602 that illustrates the simulated hot flue gas temperature output fromthe heat exchanger over time, a middle line 604 that illustrates therelatively cold simulated steam temperature at the output of the heatexchanger over time (based on the temperature profile of the flue gasentering into the heat exchanger) and a lower line 606 that indicatesthe simulated heat transfer coefficient between heat exchanger metal andsteam as determined by the system of FIG. 4 when used to model theoperation of the heat exchanger.

This example shows that, at the beginning, the simulated flue gas outlettemperature was at 1269.9 F, the simulated steam outlet temperature wasat 1195.9 F, and the simulated super-heater heat transfer coefficient(from metal to steam) was at 0.005. However, the actual steamtemperature from measurement was 1185F. Thereafter, the simulation modeltuner 414 was turned on and, as indicated in the line 606, the heattransfer coefficient was gradually adjusted by the tuner 414 to a finalvalue 0.0031, while the simulated steam temperature finally settled at1185F.

More particularly, at the time 610 indicated by the dotted line in FIG.6, the process characteristic parameter determination system was turnedon and the model tuner 414 thereof started adapting the heat transfercoefficient within a model of the heat exchanger (i.e., a firstprinciple-based model in this case) based on a difference between themeasured steam temperature and a predicted value of this steamtemperature (the line 604) as produced by a process model when the fluegas in the heat exchanger matched the temperature profile of the line602.

As illustrated in FIG. 6, over a time period of approximately 50 to 60minutes, the simulation model tuner 414 adapted the heat transfercoefficient (the line 606) as used within the process model of the heatexchanger in a manner that drove the steam temperature as predicted bythe process model (the line 604) to be equal to or near equal to themeasured steam temperature. Moreover, the simulation system was able todo so even though the input flue gas and the measured steam temperaturein the plant were changing over time. Note that, as indicated in FIG. 6,it took a relatively short period of time for the heat transfercoefficient, as used in the process model, to reach steady state, atwhich this coefficient reflected the actual heat transfer coefficient ofthe heat exchanger within the process. Moreover, the simulation systemwas able to continue to develop an accurate and up-to-date value for theheat transfer coefficient during on-going or on-line (or simulatedon-line) operation of the process. Thus, in this case, as can be seenfrom FIG. 6, after approximately 20 to 30 minutes, the heat transfercoefficient of the heat exchanger (the line 606) was determined prettyaccurately and changed very little, thereby indicating that the heattransfer coefficient as determined by the simulation model tuner 414matched the actual value of this process characteristic within theprocess and thus accurately represented the conditions within theprocess.

Of course, while the example of FIG. 6 is illustrated as determining avalue of a process characteristic parameter in the form of a heattransfer coefficient of a heat exchanger using an uncontrolled processvariable in the form of a measured and predicted steam temperature atthe output of a heat exchanger, this and other process characteristicparameters could be determined using these or other process variables.

Still further, the selection of process variables for use in the processcharacteristic parameter determination unit or simulation unit for aheat exchanger may include, but is not limited to, measured orcalculated process inputs in the form of heat exchanger inlet steamtemperature, pressure, and flow and heat exchanger inlet flue gastemperature, pressure, and flow; measured or calculated process outputsin the form of heat exchanger outlet steam pressure and flow and heatexchanger outlet flue gas pressure and flow; and matched variables inthe form of steam temperature or flue gas temperature. In these systemsof course, the process controllers could be any types of controllerincluding any control algorithm used in a distributed control system(DCS), and the simulation model tuner may use any type of controlroutine for varying the process characteristic parameter including, forexample, any feedback-type stabilizing control routine or technique,such as a PID control technique. However, the system described herein isnot limited to the use with or to the determination of heat transfercoefficients in heat exchangers.

Moreover, as noted above, the systems 400 and 500 of FIGS. 4 and 5 canbe used in on-line or in off-line operations. When used in on-lineoperations, the simulation (or process characteristic parameter valuedetermination) occurs contemporaneously with the process operation, andthus the simulation system immediately receives the process variablemeasurements and process control signals needed to perform simulationand tuning. However, due to the communication load and amount of datatypically required to perform the simulation, it is desirable if thesimulation unit (or the process characteristic determination unit) isstored in and is executed on a processor of a device near or within theplant at which this data is immediately available, such as within aprocess controller performing control of the process or the equipmentbeing modeled, a field device performing control or performingmeasurements of process variables being used in the simulation etc., inorder to reduce communication overhead within the process control systemor plant communication network.

When used in an off-line simulation system, the simulation components ormodule can first collect process data in the form of inputs necessaryfor the process model (such as process controller inputs and outputs,process variable measurements of the process variable to be predicted,and any other data needed from the process for performing simulation)and may store this data in a database, such as the data historian 28 ofFIG. 1. The simulation unit or the process characteristic parameterdetermination unit may then apply the simulation and model tuningtechniques above using the stored data in the database, as if this datawere being received in real-time. Of course, in this case, thesimulation system is not limited to operating in the same time frame asthe time frame in which the data is collected and, instead, couldoperate faster or slower than real time to adaptively change the valueof the process characteristic parameter within the process model so asto cause the process model output to match the actual measured processoutput. Moreover, in an off-line situation, in which the data ispre-stored, the simulation model tuner may actually adapt the processcharacteristic parameter to a correct value at any particular time bydetermining, for a single time, the value of the process characteristicparameter that causes the predicted value of the process variable andthe measured value of the process variable to match. In this case, theprocess model and tuner unit may operate multiple times for a singletime period or segment if so desired so as to drive the differencebetween the predicted process variable value and the actual processvariable value to be zero or near zero.

Of course, the process characteristic parameter, as produced by and usedwithin the process model 410 or 510 may be provided to a user or someother application within, for example, the user interface devices 20-22of FIG. 1 to enable a user or operator to take some action based onthese determined values or to enable a user to make further decisions orchoices within the plant. For example, the operator may take orimplement a control action or a maintenance action based on thedetermined value of the process characteristic parameter. The operatormay, for example, implement control tuning via a controller application,a maintenance procedure such as a soot-blowing procedure via amaintenance application, etc., based on the determined value of theprocess characteristic parameter. Still further, a control, maintenanceor other system may compare the determined process characteristicparameter to a threshold and, based on that determination, mayautomatically take some action within the plant, such as setting orgenerating an alarm or an alert to be displayed or sent to a user,automatically initiating a control or maintenance procedure, etc. Forexample, in the situation in which the process characteristic parameteris a heat transfer coefficient of a heat exchanger, when the heattransfer coefficient becomes too low (falls below a predeterminedthreshold), a user may manually or the system may automaticallyimplement a maintenance or control procedure (such as a soot blowing orcontroller tuning) to increase the heat transfer coefficient or tocompensate for the low heat transfer coefficient in the control of theprocess. Of course, these are just a few examples of the further actionsthat could be implemented or affected by the determination of a processcharacteristic parameter within a process, and other possible uses ofthis value exist.

Moreover, as will be understood from the discussion provided above, theprocess characteristic parameter determination unit described herein,whether used as part of a simulation system or not, may be implementedin various different manners. For an off-line simulation applicationwhere simulation and control operate on different platforms (or computersystems), the process characteristic parameter determination unit withautomatic tuning will typically need to utilize historical data.Otherwise massive amounts of live or raw data would need to becommunicated to the simulation system which may entail developingdedicated communication software and data links. By utilizing the datafrom historical data file as the actual data, the simulation and processcharacteristic parameter determination calculations can proceed in astraightforward manner as described herein. On the other hand, foron-line simulation applications where simulation and control operate onthe same platform (usually a DCS control system), the data required fortuning can be directly read from the DCS highway by the simulation orprocess characteristic parameter determination unit. The resulting valueof the determined process characteristic parameter may then becontinuously written to the simulation model in real-time.

Still further, in on-line simulations of heat exchanger models thatadapt the heat transfer coefficient of the model, the determined heattransfer coefficient can be used for intelligent soot-blowing purposesor other maintenance purposes. In particular, by continuously monitoringand adjusting the heat transfer coefficient in the proposed manner, thefouling condition of a heat transfer area can be detected in real-time.A higher value of a heat transfer coefficient will correspond to acleaner boiler section, and vice versa. As a result, this system may beused to implement an intelligent soot-blowing (ISB) application as thecontinuous heat transfer coefficient calculation can be utilized toadvise or recommend soot-blowing operations. This approach can be usedin lieu of other known heat-balance based first-principle “cleanlinessfactor” calculation methods or empirical data-driven “cleanlinessfactor” calculation methods.

More particularly, in a real-time simulation application used on, forexample a boiler based power plant, this method allows the heat transfercoefficient to be determined automatically and continuously according tothe ever changing run-time boiler conditions, and no other manual tuningor special data link, or third party software item is generally needed.In this case, it may be desirable to perform soot-blowing operationsbased on the determined heat transfer coefficient of a heat exchangereither manually or automatically. For example, when the determined heattransfer coefficient (or other process characteristic parameter orenergy transfer characteristic parameter) falls below a predeterminedthreshold, the system or application receiving the heat transfercoefficient and performing the comparison may set an alarm or an alertto be displayed to the user, indicating that a soot-blowing operationneeds to be performed. Alternatively, the system or application mayautomatically initiate a soot-blowing operation on the heat exchanger inresponse to the comparison. In this manner, soot-blowing is performedmore effectively, as this method helps to assure that soot-blowingoperations are performed when needed, but reduces the number ofunnecessary soot-blowing operations as this method limits or preventsthe initiation of soot-blowing operations when the heat transfercoefficient of the heat exchanger is still within an acceptable range.

In one case, it may be desirable to limit the automatic initiation ofthe soot-blowing operations or the generation of an alarm, an alert orother notification to a user to comparisons made between the heattransfer coefficient and the threshold made when the difference signalis at or near zero (i.e., within a predetermined threshold around zero,which threshold may be, for example, user selectable). That is, thesystem or application performing the soot-blowing decision may alsoreceive the value of the difference signal and determine when themagnitude of the difference signal is below a threshold (i.e., nearzero) or may receive an indication when magnitude of the differencesignal is below a threshold (i.e., near zero) generated by thesimulation module implementing the difference unit (412 or 512 forexample of FIGS. 4 and 5, respectively) and the receiving system orapplication may operate to only initiate manual or automaticsoot-blowing operations when the difference signal is near zero. Thischeck prevents soot-blowing initiations when the tuning and simulationsystem is not yet tuned (and thus the heat transfer coefficient maystill be incorrect).

Moreover, the design mechanism and calculation techniques describedherein are not limited to heat transfer coefficient (which is a type ofenergy transfer coefficient) modeling and auto-tuning. These mechanismsand techniques can be useful for other applications as well. Forexample, these methods can be used to automatically calibrate fuelheating values (which is also an energy transfer coefficient) used in asimulation model that relies on this heating value (or BTU content) tocalculate combustion temperature and/or the amount of power generation.In this case, the equivalent control input can be the fuel flow, and theequivalent process/model output (the variable to be matched) can beselected as the flue gas temperature or the generated power. However,this use is but one other example of how these techniques can be used todetermine process characteristic parameters, and many other uses arepossible.

While the process characteristic parameter determination and/orsimulation systems described herein can be used in any desired type ofplant to simulate, for example, material flow through the plant(liquids, gases or even solids), one example distributed simulationsystem is described herein as being used to simulate a power generationplant being controlled using distributed control techniques. However,the process characteristic parameter determination and simulationtechniques described herein can be used in other types of plants andcontrol systems, including industrial manufacturing and processingplants, water and waste water treatment plants, etc. and can be usedwith control systems implemented centrally or as distributed controlsystems.

When implemented in software, any of the process characteristicparameter determination software, simulation software and/or simulationmodules described herein may be stored in any computer readable memorysuch as on a magnetic disk, a laser disk, or other storage medium, in aRAM or ROM of a computer or processor, etc Likewise, this software orthese modules may be delivered to a user, a process plant or an operatorworkstation using any known or desired delivery method including, forexample, on a computer readable disk or other transportable computerstorage mechanism or over a communication channel such as a telephoneline, the Internet, the World Wide Web, any other local area network orwide area network, etc. (which delivery is viewed as being the same asor interchangeable with providing such software via a transportablestorage medium). Furthermore, this software may be provided directlywithout modulation or encryption or may be modulated and/or encryptedusing any suitable modulation carrier wave and/or encryption techniquebefore being transmitted over a communication channel.

Although the example systems disclosed herein are disclosed asincluding, among other components, software and/or firmware executed onhardware, it should be noted that such systems are merely illustrativeand should not be considered as limiting. For example, it iscontemplated that any or all of these hardware, software, and firmwarecomponents could be embodied exclusively in hardware, exclusively insoftware, or in any combination of hardware and software. Accordingly,while the example systems described herein are described as beingimplemented in software executed on a processor of one or more computerdevices, persons of ordinary skill in the art will readily appreciatethat the examples provided are not the only way to implement suchsystems.

Thus, while the present invention has been described with reference tospecific examples, which are intended to be illustrative only and not tobe limiting of the invention, it will be apparent to those of ordinaryskill in the art that changes, additions or deletions may be made to thedisclosed embodiments without departing from the spirit and scope of theinvention.

1. A process characteristic parameter determination system for use indetermining a value of a process characteristic of a process,comprising: a process model unit including a process model that modelsthe operation of a process based on a set of process inputs to theprocess model and based on a process characteristic parameter thatreflects a process characteristic of the process, wherein the processmodel unit develops a predicted value of a process variable based on theprocess inputs to the process model and the process characteristicparameter; a difference unit coupled to the process model unit and tothe process, the difference unit having a first input that receives aprocess variable value for the process variable as measured ordetermined within the process and a second input that receives thepredicted value of the process variable as determined by the processmodel unit, wherein the difference unit determines a difference signalindicating a difference between the first input and the second input;and a tuner unit that changes the value of the process characteristicparameter of the process model based on the difference signal.
 2. Theprocess characteristic parameter determination system of claim 1,wherein the tuner unit includes tuner logic to drive the differencesignal towards zero.
 3. The process characteristic parameterdetermination system of claim 1, wherein the process model is firstprinciple-based model of the process.
 4. The process characteristicparameter determination system of claim 1, wherein process model is anempirical model of the process.
 5. The process characteristic parameterdetermination system of claim 1, wherein tuner unit includes a feedbackcontrol logic to determine the manner in which to change the value ofthe process characteristic parameter within the process model.
 6. Theprocess characteristic parameter determination system of claim 1,wherein the process model unit includes a simulated process controllerhaving control logic to produce a simulated control signal value as thepredicted value of the process variable, wherein the process modelproduces a predicted value of a further process variable and wherein thesimulated process controller has a first input to receive a set pointand a second input to receive the predicted value of the further processvariable from the process model and wherein the simulated processcontroller uses the control logic to develop the simulated controlsignal value as the predicted value of the process variable based on theset point and the predicted value of the further process variable. 7.The process characteristic parameter determination system of claim 6,wherein the further process variable is a controlled process variablewithin the process and wherein the simulated process controller receivesthe predicted value of the further process variable as a controlledvariable feedback signal, and receives the set point as a set point forthe further process variable from the process.
 8. The processcharacteristic parameter determination system of claim 6, wherein thedifference unit receives the simulated process control signal as thepredicted value of the process variable at the second input and receivesa control signal from a process controller within the process as theprocess variable value at the first input.
 9. The process characteristicparameter determination system of claim 1, wherein difference unit is asummer that subtracts one of the first and second inputs from the otherone of the first and second inputs to create the difference signal. 10.The process characteristic parameter determination system of claim 1,wherein the process variable is an uncontrolled variable within theprocess, and wherein the process model unit produces the predicatedvalue of the process variable as a predicted value of the uncontrolledvariable.
 11. The process characteristic parameter determination systemof claim 1, wherein the process variable is process control signalwithin the process, and wherein the process model unit produces thepredicated value of the process variable as a predicted value of theprocess control signal.
 12. A method of simulating a process,comprising: simulating the operation of the process to produce apredicted value of a process variable using a process model unit thatincludes a process model that models the operation of a process based ona set of process inputs to the process model and based on a processcharacteristic parameter that reflects a process characteristic of theprocess to develop a predicted value of a process variable based on theprocess inputs to the process model and the process characteristicparameter; obtaining an actual value of the process variable from theprocess; determining a difference between the actual value of theprocess variable and the predicted value of the process variableproduced by the process model unit; and adjusting the value of theprocess characteristic parameter of the process model based on thedifference signal to drive the difference signal towards zero.
 13. Themethod of simulating a process according to claim 12, wherein simulatingthe operation of the process to produce a predicted value of a processvariable includes using the process model to produce a predicted valueof an uncontrolled process variable within the process as the predictedvalue of the process variable and wherein obtaining an actual value ofthe process variable from the process includes obtaining a measuredvalue of the uncontrolled process variable in the process.
 14. Themethod of simulating a process according to claim 12, wherein simulatingthe operation of the process to produce a predicted value of a processvariable includes using the process model to produce a predicted valueof a controlled process variable within the process, and simulating thecontrol system of the process using the predicted value of thecontrolled process variable to produce a simulated control signal valueas the predicted value of the process variable and wherein obtaining anactual value of the process variable from the process includes obtaininga value of a process control signal developed to control the controlledprocess variable from a process controller within the process.
 15. Themethod of simulating a process according to claim 14, further includingobtaining a value of a set point used by the process controller withinthe process and wherein simulating the control system of the processincludes using the set point value and the predicted value of thecontrolled process variable to produce the simulated control signalvalue as the predicted value of the process variable.
 16. The method ofsimulating a process according to claim 15, further including using thesame control logic in the simulated process controller as used in theprocess controller within the process.
 17. The method of simulating aprocess according to claim 12, wherein adjusting the value of theprocess characteristic parameter of the process model includes using afeedback control routing to determine a manner in which to adjust thevalue of the process characteristic parameter of the process model basedon the difference signal.
 18. The method of simulating a processaccording to claim 12, wherein simulating the operation of the processto produce a predicted value of a process variable using a process modelincludes using a first principle-based model of the process within theprocess model.
 19. The method of simulating a process according to claim12, wherein simulating the operation of the process to produce apredicted value of a process variable using a process model includesusing an empirical-based model of the process within the process model.20. The method of simulating a process according to claim 12, whereinsimulating the operation of the process includes producing a predictedvalue of an uncontrolled process variable as the predicted value of theprocess variable.
 21. The method of simulating a process according toclaim 12, wherein simulating the operation of the process includesproducing a simulated value of a process control signal as the predictedvalue of the process variable.
 22. The method of simulating a processaccording to claim 12, wherein the process characteristic parameter is aheat transfer coefficient of a heat exchanger.
 23. The method ofsimulating a process according to claim 12, further including performingthe steps of simulating the operation of the process to produce thepredicted value of the process variable, obtaining the actual value ofthe process variable from the process, determining a difference betweenthe actual value of the process variable and the predicted value of theprocess variable and adjusting the value of the process characteristicparameter of the process model in real-time while the process isoperating.
 24. The method of simulating a process according to claim 12,further including performing the steps of simulating the operation ofthe process to produce the predicted value of the process variable,obtaining the actual value of the process variable from the process,determining a difference between the actual value of the processvariable and the predicted value of the process variable and adjustingthe value of the process characteristic parameter of the process modelbased on process data stored in a database.
 25. The method of simulatinga process according to claim 12, further including performing the stepsof simulating the operation of the process to produce the predictedvalue of the process variable, obtaining the actual value of the processvariable from the process, determining a difference between the actualvalue of the process variable and the predicted value of the processvariable and adjusting the value of the process characteristic parameterof the process model based on process data stored in a database off-lineafter operation of the process.
 26. A method of determining an energytransfer coefficient of process equipment, comprising: simulating theoperation of the process equipment within a process to produce apredicted value of a process variable using a process model unit thatuses a process model that models the operation of the process equipmentbased on a set of process inputs to the process model and based on anenergy transfer coefficient of the process equipment to develop apredicted value of a process variable based on the process inputs to theprocess model and the energy transfer coefficient; obtaining an actualvalue of the process variable from the process; determining a differencebetween the actual value of the process variable and the predicted valueof the process variable produced by the process model unit; adjustingthe value of the energy transfer coefficient of the process model basedon the difference signal to drive the difference signal towards zero;and determining the energy transfer coefficient of the process equipmentas the energy transfer coefficient of the process model when thedifference signal is near zero.
 27. The method of determining an energytransfer coefficient of process equipment according to claim 26, whereinsimulating the operation of the process equipment to produce a predictedvalue of a process variable includes using the process model to producea predicted value of an uncontrolled process variable within the processas the predicted value of the process variable and wherein obtaining anactual value of the process variable from the process includes obtaininga measured value of the uncontrolled process variable from the process.28. The method of determining an energy transfer coefficient of processequipment according to claim 26, wherein simulating the operation of theprocess equipment to produce a predicted value of a process variableincludes using the process model to produce a predicted value of acontrolled process variable within the process, and simulating a controlsystem of the process equipment using the predicted value of thecontrolled process variable to produce a simulated control signal valueas the predicted value of the process variable and wherein obtaining anactual value of the process variable from the process includes obtaininga value of a process control signal developed to control the controlledprocess variable from a process controller used to control the processequipment.
 29. The method of determining an energy transfer coefficientof process equipment according to claim 26, wherein the energy transfercoefficient is a heat transfer coefficient of a heat exchanger.
 30. Themethod of determining an energy transfer coefficient of processequipment according to claim 26, wherein the energy transfer coefficientis a fuel heating value.
 31. The method of determining an energytransfer coefficient of process equipment according to claim 26, whereinsimulating the operation of the process equipment within the process toproduce a predicted value of a process variable using a process modelunit includes using a process model that is a first principle-basedprocess model.
 32. A method of performing soot-blowing in a heatexchanger of a process, comprising: simulating the operation of the heatexchanger of the process to produce a predicted value of a processvariable using a process model unit that includes a process model thatmodels the operation of the heat exchanger based on a set of processinputs to the process model and based on a heat transfer characteristicparameter of the heat exchanger equipment to develop a predicted valueof a process variable based on the process inputs to the process modeland the heat transfer characteristic parameter; obtaining an actualvalue of the process variable from the process; determining a differencesignal indicating a difference between the actual value of the processvariable and the predicted value of the process variable produced by theprocess model; adjusting the value of the heat transfer characteristicparameter of the process model based on the difference signal to drivethe difference signal towards zero; and using the heat transfercharacteristic parameter of the process model to determine whether toinitiate a soot-blowing operation in the heat exchanger.
 33. A method ofperforming soot-blowing in a heat exchanger of a process of claim 32,wherein using the heat transfer characteristic parameter to determinewhether to initiate a soot-blowing operation includes comparing the heattransfer characteristic parameter to a threshold and initiating asoot-blowing operation when the heat transfer characteristic parameteris below the threshold.
 34. The method of performing soot-blowing in aheat exchanger of a process of claim 33, further including automaticallyinitiating the soot-blowing operation when the heat transfercharacteristic parameter is below the threshold.
 35. The method ofperforming soot-blowing in a heat exchanger of a process of claim 32,wherein using the heat transfer characteristic parameter to determinewhether to initiate a soot-blowing operation includes comparing the heattransfer characteristic parameter to a threshold and automaticallynotifying a user that a soot-blowing operation needs to be initiatedwhen the heat transfer characteristic parameter is below the threshold.36. The method of performing soot-blowing in a heat exchanger of aprocess of claim 32, wherein the heat transfer characteristic parameterreflects a heat transfer coefficient of the heat exchanger.
 37. Themethod of performing soot-blowing in a heat exchanger of a process ofclaim 32, wherein simulating the operation of the heat exchanger toproduce a predicted value of a process variable includes using theprocess model to produce a predicted value of an uncontrolled processvariable within the process as the predicted value of the processvariable and wherein obtaining an actual value of the process variablefrom the process includes obtaining a measured value of the uncontrolledprocess variable in the process.
 38. The method of performingsoot-blowing in a heat exchanger of a process of claim 32, whereinsimulating the operation of the heat exchanger to produce a predictedvalue of a process variable includes using the process model to producea predicted value of a controlled process variable within the process,and simulating a control system of the process equipment using thepredicted value of the controlled process variable to produce asimulated control signal value as the predicted value of the processvariable and wherein obtaining an actual value of the process variablefrom the process includes obtaining a value of a process control signaldeveloped to control the controlled process variable from a processcontroller used to control the heat exchanger.
 39. The method ofperforming soot-blowing in a heat exchanger of a process of claim 32,wherein using the heat transfer characteristic parameter of the processmodel to determine whether to initiate a soot-blowing operation in theheat exchanger includes using the heat transfer characteristic parameterof the process model to determine when to initiate a soot blowingoperation only when the difference signal is near zero.
 40. A simulationsystem for simulating the operation of a process, comprising: aplurality of simulation modules, each of the simulation modules beingstored in a computer readable medium and operable on a processor andbeing communicatively connected to one or more of the other simulationmodules, wherein each of the simulation modules includes a process modelthat performs modeling of a different portion of the process; whereinone of the simulation modules includes; a process model unit including aprocess model that executes on a processor to model the operation of aparticular portion of the process based on a set of process inputs tothe process model and based on a process characteristic parameter thatreflects a process characteristic associated with the particular portionof the process, wherein the process model unit when operated develops apredicted value of a process variable based on the process inputs to theprocess model and the process characteristic parameter; a differenceunit that executes on a processor and that is coupled to the processmodel unit and to the process, the difference unit having a first inputthat receives a process variable value for the process variable asmeasured or determined within the process and a second input thatreceives the predicted value of the process variable as determined bythe process model unit, wherein the difference unit determines adifference signal indicating a difference between the first input andthe second input; and a tuner unit that executes on a processor tochange the value of the process characteristic parameter of the processmodel based on the difference signal; and wherein the one of thesimulation modules operates to communicate an output of the processmodel unit as an input to another one of the simulation modules.
 41. Thesimulation system for simulating the operation of a process of claim 40,wherein the process model is first principle-based model of theparticular portion of the process.
 42. The simulation system forsimulating the operation of a process of claim 40, wherein the processmodel unit includes a simulated process controller having control logicto produce a simulated control signal value as the predicted value ofthe process variable, wherein the process model produces a predictedvalue of a further process variable and wherein the simulated processcontroller has a first input to receive a set point and a second inputto receive the predicted value of the further process variable from theprocess model.
 43. The simulation system for simulating the operation ofa process of claim 42, wherein the further process variable is acontrolled process variable within the process and wherein the simulatedprocess controller receives the predicted value of the further processvariable as a controlled variable feedback signal, and receives the setpoint as a set point for the further process variable from the process.44. The simulation system for simulating the operation of a process ofclaim 43, wherein the difference unit receives the simulated processcontrol signal value as the predicted value of the process variable atthe second input and receives a control signal value from a processcontroller within the process as the process variable value at the firstinput.
 45. The simulation system for simulating the operation of aprocess of claim 40, wherein the process variable is an uncontrolledvariable within the process, and wherein the process model unit producesthe predicated value of the process variable as a predicted value of theuncontrolled variable.
 46. The simulation system for simulating theoperation of a process of claim 40, wherein tuner unit includes afeedback control logic to determine the manner in which to change thevalue of the process characteristic parameter within the process model.47. The simulation system for simulating the operation of a process ofclaim 40, wherein the process variable is a process control signalwithin the process, and wherein the process model unit produces thepredicated value of the process variable as a predicted value of theprocess control signal.
 48. The simulation system for simulating theoperation of a process of claim 40, wherein the plurality of simulationmodules are stored in and executed in separate computer devicesconnected by one or more communication channels.
 49. The simulationsystem for simulating the operation of a process of claim 40, whereinthe plurality of simulation modules operate on-line during operation ofthe process.
 50. The simulation system for simulating the operation of aprocess of claim 40, wherein the one of the simulation modules is storedin a device associated with controlling the particular portion of theprocess equipment.
 51. The simulation system for simulating theoperation of a process of claim 40, further including a data historianthat stores process data generated in the process, wherein the pluralityof simulation modules are communicatively coupled to the data historianto receive the process data for the process variable to operate off-linewith respect to operation of the process.